Trends in SML intertidal data (sessile species)
Kylla Benes
12/16/2020
What species are in the rocky intertidal data?
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## [73] 31.000000 87.000000 89.000000 84.000000 81.000000 32.000000
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## [229] 3.100000 18.600000 43.700000 18.700000 0.700000 45.900000
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## [253] 33.500000 4.800000 20.100000 45.100000 3.700000 37.200000
## [259] 34.100000 24.800000 52.700000 47.200000 2.800000 12.100000
## [265] 28.900000 1.250000 557.000000 46.750000 40.600000 34.400000
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## [283] 46.500000 3.900000 70.200000 17.600000 38.400000 38.200000
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## [337] 26.300000 22.600000 0.450000 13.600000 27.150000 5.330000
## [343] 14.200000 31.100000 0.440000 26.700000 30.650000 0.350000
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## [355] 0.770000 11.540000 15.400000 12.900000 0.340000 4.100000
## [361] 13.560000 44.100000 5.210000 11.320000 21.740000 34.780000
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## [379] 1.190000 4.410000 41.120000 12.140000 46.740000 0.740000
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## [391] 9.960000 3.830000 0.380000 17.120000 26.040000 41.090000
## [397] 5.480000 10.270000 87.960000 4.630000 20.700000 0.410000
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## [439] 66.360000 1.810000 13.800000 30.800000 24.600000 69.700000
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## [523] 18.100000 57.100000 7.600000 32.900000 4.300000 65.300000
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## [535] 51.600000 2.400000 30.200000 45.300000 16.100000 9.300000
## [541] 48.600000 57.300000 50.700000 71.100000 25.700000 6.600000
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## [565] 4.670000 71.400000 25.900000 11.100000 55.600000 72.400000
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## [577] 15.620000 90.620000 71.870000 84.370000 53.100000 4.685000
## [583] 4.690000 2.080000 0.781000 2.089000 29.860000 10.200000
## [589] 7.812500 7.812000 7.031000 70.310000 4.689000 13.880000
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## [619] 21.880000 71.860000 1.563000 90.625000 53.125000 17.190000
## [625] 98.500000 12.090000 81.860000 5.580000 25.330000 6.180000
## [631] 3.090000 31.950000 13.700000 79.500000 12.800000 49.800000
## [637] 2.600000 2.750000 45.600000 15.200000 96.300000 88.600000
## [643] 83.600000 65.900000 28.800000 10.800000 58.900000 1.550000
## [649] 98.400000 6.200000 45.150000 78.100000 11.800000 25.600000
## [655] 33.800000 13.300000 20.800000 21.300000 8.100000 36.300000
## [661] 13.100000 35.800000 25.800000 45.800000 42.300000 26.100000
## [667] 28.500000 55.800000 19.600000 73.400000 20.300000 29.100000
## [673] 39.100000 62.700000 93.600000 64.100000 82.800000 0.780000
## [679] 88.500000 88.150000 11.850000 77.700000 88.300000 11.200000
## [685] 42.550000 39.600000 54.400000 62.800000 60.400000 77.600000
## [691] 89.200000 94.200000 34.500000 680.000000 78.500000 72.500000
## [697] 14.500000 7.197485 73.500000 0.020000 68.500000 32.800000
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## [715] 10.900000 60.800000 7.900000 17.200000 98.600000 77.500000
## [721] 92.500000 86.500000 97.333333 1.154701 13.500000 0.050000
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## [733] 0.070000 0.550000 0.040000 0.150000 0.850000 0.060000
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## [745] 0.330000 0.008000 0.992000 0.190000 0.941000 0.009000
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## [757] 0.140000 0.260000 0.130000 0.290000 0.110000 0.090000
## [763] 0.180000 0.160000 0.170000 82.500000 7.330000 71.500000
## [769] 35.104131 27.666667 26.398954 18.333333 16.931233 120.000000Let’s Plot Trends
Here are some functions we’ll apply to each species. I’ll then fit mixed models with all of them using
with a gaussian error. Note, it’s count, so, I could use Poisson, but, that needs to be way more tuned to each model - this should be fine for a first cut?
Here’s how we’ll do it:
tseries_plot <- function(data){
ggplot(data,
aes(y = Percent_cover, x = Year,
color = Transect)) +
stat_summary(fun.data = mean_se) +
stat_summary(fun.data = mean_se, geom = "line") +
labs(color = "Transect",
y = "Percent Cover ± SE", x = "Year")
}
tseries_fun <- function(data){
glmmTMB(Percent_cover ~ Year + (1|Transect),
data = data,
family = gaussian)
}
generate_output <- function(data){
cat(paste0("## ", data$Organism[1], " ", data$Organism[1], " \n<br>"))
tseries_plot(data) %>% print()
# tseries_fun(adf) %>%
# car::Anova(test.statistic = "Chisq") %>%
# tidy() %>%
# knitr::kable("html", digits = 3) %>%
# kableExtra::kable_styling() %>%
# print
#
cat("\n\n")
}Now let’s purrr::walk() through the whole shebang.
walk(split(data, data$Organism), generate_output)Acrosiphonia Acrosiphonia
Ahnfeltia plicata Ahnfeltia plicata
Alaria esculenta (canopy) Alaria esculenta (canopy)
Alaria esculenta (primary) Alaria esculenta (primary)
Anomia simplex Anomia simplex
Antithamnion Antithamnion
Aplidium Aplidium
Arthopyrenia Arthopyrenia
Ascophyllum nodosum (canopy) Ascophyllum nodosum (canopy)
Ascophyllum nodosum (primary) Ascophyllum nodosum (primary)
Audouinella Audouinella
Bangia atropurpurea Bangia atropurpurea
Bare rock Bare rock
Black zone Black zone
Bonnemaisonia hamifera Bonnemaisonia hamifera
Bonnemaisonia hamifera (Trailiella phase) Bonnemaisonia hamifera (Trailiella phase)
Botrylloides violaceus Botrylloides violaceus
Botryllus schlosseri Botryllus schlosseri
Brown ground Brown ground
Brown lichen Brown lichen
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Bugula neritina Bugula neritina
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Callophyllis Callophyllis
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Ceramium virgatum (canopy) Ceramium virgatum (canopy)
Ceramium virgatum (primary) Ceramium virgatum (primary)
Chaetomorpha Chaetomorpha
Chondrus crispus (canopy) Chondrus crispus (canopy)
Chondrus crispus (primary) Chondrus crispus (primary)
Chorda filum Chorda filum
Chordaria flagelliformis Chordaria flagelliformis
Cladophora Cladophora
Cladostephus spongiosus Cladostephus spongiosus
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Clathromorphum circumscriptum Clathromorphum circumscriptum
Codium fragile (canopy) Codium fragile (canopy)
Codium fragile (primary) Codium fragile (primary)
Colpomenia peregrina Colpomenia peregrina
Corallina officinalis Corallina officinalis
Coralline (crust) Coralline (crust)
Cryptosula pallasiana Cryptosula pallasiana
Cyanobacteria Cyanobacteria
Cystoclonium purpureum Cystoclonium purpureum
Devaleraea ramentacea Devaleraea ramentacea
Diadumene lineata Diadumene lineata
Diatom Diatom
Didemnum vexillum Didemnum vexillum
Diplosoma Diplosoma
Dumontia concortum Dumontia concortum
Dynamena pumila Dynamena pumila
Ectocarpus Ectocarpus
Elachista fucicola Elachista fucicola
Electra pilosa Electra pilosa
Erythrotrichia carnea Erythrotrichia carnea
Euthora cristata Euthora cristata
Feather Feather
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Fucus (canopy) Fucus (canopy)
Fucus (primary) Fucus (primary)
Fucus distichus (canopy) Fucus distichus (canopy)
Fucus distichus (primary) Fucus distichus (primary)
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Fucus spiralis (canopy) Fucus spiralis (canopy)
Fucus vesiculosus (canopy) Fucus vesiculosus (canopy)
Fucus vesiculosus (primary) Fucus vesiculosus (primary)
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Giffordia granulosa Giffordia granulosa
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Halichondria Halichondria
Hiatella arctica Hiatella arctica
Hildenbrandia rubra Hildenbrandia rubra
Hippothoa hyalina Hippothoa hyalina
Leathesia marina Leathesia marina
Lithophyllum Lithophyllum
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Little round green things Little round green things
Mastocarpus stellatus (canopy) Mastocarpus stellatus (canopy)
Mastocarpus stellatus (crust) Mastocarpus stellatus (crust)
Mastocarpus stellatus (primary) Mastocarpus stellatus (primary)
Membranipora membranacea Membranipora membranacea
Metridium senile Metridium senile
Modiolus modiolus Modiolus modiolus
Monostroma Monostroma
Mytilus edulis Mytilus edulis
Nemalion helminthoides Nemalion helminthoides
Palmaria palmata Palmaria palmata
Palmellococcus Palmellococcus
Petalonia fascia Petalonia fascia
Phymatolithon lenormandii Phymatolithon lenormandii
Plumaria plumosa Plumaria plumosa
Polysiphonia (canopy) Polysiphonia (canopy)
Polysiphonia (primary) Polysiphonia (primary)
Porphyra Porphyra
Prasiola stipitata Prasiola stipitata
Protectocarpus speciosus Protectocarpus speciosus
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Ptilota serrata Ptilota serrata
Pylaiella littoralis Pylaiella littoralis
Ralfsia Ralfsia
Red lichen Red lichen
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Rhizoclonium tortuosum Rhizoclonium tortuosum
Saccharina latissima (canopy) Saccharina latissima (canopy)
Saccharina latissima (primary) Saccharina latissima (primary)
Saccorhiza dermatodea Saccorhiza dermatodea
Schizoporella unicornis Schizoporella unicornis
Scytosiphon lomentaria Scytosiphon lomentaria
Semibalanus balanoides Semibalanus balanoides
Semibalanus balanoides (dead) Semibalanus balanoides (dead)
Shell hash Shell hash
Spermothamnion repens Spermothamnion repens
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Sphacelaria Sphacelaria
Sphacelaria cirrosa Sphacelaria cirrosa
Spirorbis Spirorbis
Spongomorpha aeruginosa Spongomorpha aeruginosa
Tricellaria Tricellaria
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Ulva intestinalis Ulva intestinalis
Ulva lactuca Ulva lactuca
Unknown green crust Unknown green crust
## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
Verrucaria Verrucaria
Vertebrata lanosa Vertebrata lanosa
